The NBA announced Thursday that it will install motion-tracking cameras in every arena this season to provide coaches, players and fans reams of data aimed at pulling back the curtain on what it takes to succeed at basketball's highest level.

The NBA has partnered with STATS on the SportVU cameras, and the relationship has grown from a single arena during the 2009 NBA Finals into a league-wide initiative that will be up and ready for the start of this season. The technology can monitor every move a player makes on the court, gauge how tired he is and can even keep an eye on the job referees are doing.

The project makes the NBA the first professional basketball league in the world, and the first sports league in the United States, to use the technology to analyze player movement.

"At this point, given the value of the data both at the team level and the league level, and the promise that it holds for unlocking some of the secrets for what makes great basketball teams, both for our basketball operations people and for our fans at home, we thought it was the right time to make it a league-wide effort," NBA executive vice president of operations and technology Steve Hellmuth said.

The system of six cameras and accompanying software that delivers the data was first used in Orlando, Fla., during the 2009 Finals between the Magic and the Los Angeles Lakers. In the last three years, 15 teams purchased the system from STATS, which is owned jointly by The Associated Press and 21st Century Fox, to put to use in their home arenas, arming themselves with data that could be tailored virtually any way teams want.

Want to see how successful Ricky Rubio was at guarding Russell Westbrook? The system could break down the shooting percentages and results of each head-to-head possession.

Want to get an idea how close to 100 percent Kevin Love was in his first few games back from a broken hand? The system could send information to the team trainers and doctors about his endurance and how quickly he is tiring during a game, thereby painting the most accurate picture possible of his recovery.

Want to see how many times Al Jefferson touched the ball on the left block in the first half? The system could send information to an iPad that showed the location of every one of his possessions and allowed coaches to make adjustments on the fly.

"It's gone from an interesting concept to actually something that's allowing them to take action on a daily basis," STATS Vice President Brian Kopp said. "That was the big change that we knew we needed to make in order for this to be adopted by the teams. What we always wanted to do was to be at this point and have a partnership with the league itself."

When only 15 teams were using the technology, scores of games were being missed, which in turn made the sample sizes incomplete. Now, every game and every player will be monitored every night, creating a much more complete database.

"It's really evolved from a high-level concept, something that seemed interesting, to something that could be actionable and used on a daily basis," Kopp said.

And the players aren't the only ones who will be watched by the eye in the sky. Hellmuth said new executive vice president of basketball operations Rod Thorn will be able to use data on referees to more completely evaluate their performances.

Fans will have access to some of the data through presentations by the teams at the arena, on NBA.com and on NBA TV. Hellmuth and Kopp think the most helpful information will come on the defensive end, where stats like blocked shots and steals, while helpful, don't always paint the most accurate picture of the league's best defensive players.

"What this can measure is both shooting efficiency and shooting frequency," Hellmuth said. "When a defender's in the paint or in the area, players can always choose not to shoot when he's in the area. And then also how much does that defender reduce the shooting average of the players he's defending. These are some of the secrets this unlocks."

The algorithms that are used to interpret the data are constantly being refined, Kopp said, and now can identify certain plays -- like a pick-and-roll -- and defensive rotations. Now that they are poised to have even more data, the strength and breadth of the information the system can provide should only improve, he said.

"I really do think we've just scratched the surface on how we can use this," Kopp said. "I think the next few years will be fun as we have more data to work with."

video games are gonna be so SICK in a few years,

Zach Lowe posted some of Toronto's for Sportsvu during last season...

http://www.grantland.com/story/_/id/9068903/the-toronto-raptors-sportvu-cameras-nba-analytical-revolution

http://www.grantland.com/blog/the-triangle/post/_/id/55110/the-sportvu-follow-up-answering-the-most-common-questions-and-more-ghost-players

and this one a couple of days ago

http://www.grantland.com/blog/the-triangle/post/_/id/73501/seven-ways-the-nbas-new-camera-system-can-change-the-future-of-basketball

dk7th wrote:this development furthers the cause of people who like an advanced-stat, moneyball approach to assess the value of any player's contributions. it should help correct the problem of bloated salaries for overrated players.

But these numbers are still only a part of the equation. Different sport, but Jeter is always my favorite regarding the inconsistency of sabermetrics, which is being applied to other sports. Sabermetrics diminish Jeter's play and contribution based solely on these stats, many of which are bloated and all of which are highly manipulatable (hey, new word!). If, however, you are a baseball fan, and you have watched Jeter play on a regular basis, you know that he has consistently made significant contributions to the team and their ability to win. He's got rings, none of which were as likely to acquired without his contribution (or Mo's). What's more these numbers like to toss out the traditional stats which are still tremendously valid.

smackeddog wrote:Not a fan of this at all- why not just cancel the season and run a computer simulation instead.

that's where we're going

sports gaming is the new sports

smackeddog wrote:Not a fan of this at all- why not just cancel the season and run a computer simulation instead.

"don't criticize what you can't understand." -- bob dylan

Thursday, January 20, 2011


Sabermetric basketball statistics are too flawed to work

You know all those player evaluation statistics in basketball, like "Wins Produced," "Player Evaluation Rating," and so forth? I don't think they work. I've been thinking about it, and I don't think I trust any of them enough put much faith in their results.

That's the opposite of how I feel about baseball. For baseball, if the sportswriter consensus is that player A is an excellent offensive player, but it turns out his OPS is a mediocre .700, I'm going to trust OPS. But, for basketball, if the sportswriters say a guy's good, but his "Wins Produced" is just average, I might be inclined to trust the sportswriters.

I don't think the stats work well enough to be useful.

I'm willing to be proven wrong. A lot of basketball analysts, all of whom know a lot more about basketball than I do (and many of whom are a lot smarter than I am), will disagree. I know they'll disagree because they do, in fact, use the stats. So, there are probably arguments I haven't considered. Let me know what those are, and let me know if you think my own logic is flawed.

------

The most obvious problem is rebounds, which I've posted about many times (including these posts over the last couple of weeks). The problem is that a large proportion of rebounds are "taken" from teammates, in the sense that if the player credited with the rebound hadn't got it, another teammate would have.

We don't know the exact numbers, but maybe 70% of defensive and 50% of offensive rebounds are taken from a teammates' total.

More importantly, it's not random, and it's not the same for all players. Some rebounders will cover much more of other players' territory than others. So when player X had a huge rebounding total, we don't know whether he's just good at rebounds, whether he's just taking them from teammates, or whether it's some combination of the two.

So, even if we decide to take 70% of every defensive rebound, and assign it to teammates, we don't know that's the right number for the particular team and rebounder. This would lead to potentially large errors in player evaluations.

The bottom line: we know exactly what a rebound is worth for a team, but we don't know which players are responsible, in what proportion, for the team's overall performance.

------

Now, that's just rebounds. If that were all there were, we could just leave that out of the statistic, and go with what we have. But there's a similar problem with shooting accuracy.

I ran the same test for shooting that I ran for rebounds. For the 2008-09 season, I ran regression for each of the five positions. Each row of the regression was a single team for that year, and I checked how each position's shooting (measured by eFG%) affected the average of the other four positions (the simple average, not weighted by attempts).

It turns out that there is a strong positive correlation in shooting percentage among teammates. If one teammate shoots accurately, the rest of the team gets carried along.

Here are the numbers (updated, see end of post):

PG: slope 0.30, correlation 0.63
SG: slope 0.40, correlation 0.62
SF: slope 0.26, correlation 0.27
PF: slope 0.28, correlation 0.27
-C: slope 0.27, correlation 0.43

To read one line off the chart: for every one percentage point increase in shooting percentage by the SF (say, from 47% to 48%), you saw an increase of 0.26% in each of his teammates (say, from 47% to 47.26%).

The coefficients are a lot more important than they look at first glance, because they represent a change in the average of all four teammates. Suppose all five teammates took the same number of shots (which they don't, but never mind right now). That means that when the SF makes one extra field goal, each teammate also makes an extra 0.26, for a team team total of 1.04 extra field goals.

That's a huge effect.

And, it makes sense, if my logic is right (correct me if I'm wrong). Suppose you have a team where everyone has a talent of .450, but then you get a new guy on the team (player X) with a talent of .550. You're going to want him to shoot more often than the other players. For instance, if X and another guy are equally open for a roughly equal shot, you're going to want to give the ball to X. Even if Y is a little more open than X, you'll figure that X will still outshoot Y -- maybe not .550 to .450, but, in this situation, maybe .500 to .450. So X gets the ball more often.

But, then, the defense will concentrate a little more on X, and a little less on the .450 guys. That means X might see his percentage drop from .550 to .500, say. But the extra attention to X creates more open shots for the .450 guys, and they improve to (say) .480 each.

Most of the new statistics simply treat FG% as if it's solely the achievement of the player taking the shot, when, it seems, it is very significantly influenced by his teammates.

------

Some of that, of course, might be that teams with good players tend to have other good players; that is, it's all correlation, and not causation. But there's evidence that's not the case, as illustrated by a recent debate on the value of Carmelo Anthony.

Last week, Nate Silver showed that if you looked at Carmelo Anthony's teammates' performance, and then looked at that performance when Anthony wasn't on their team, you see a difference of .038 in shooting percentage. That's huge -- about 15 wins a season.

Dave Berri responded with three criticisms. First, that Silver weighted by player instead of by game; second, that Silver hadn't considered the age of the teammates (since very young players improve anyway as they get older); and, third, that if you control for age and a bunch of other things, the results aren't statistically significant from zero. (However, Berri didn't post the full regression results, and did not claim that his estimate was different from .038.)

Finally, over at Basketball Prospectus, Kevin Pelton ran a similar analysis, but within games instead of between seasons (which eliminates the age problem, and a bunch of other possible confounding variables). He found a difference of .028. Not quite as high as Silver, but still pretty impressive. Furthermore, a similar analysis of all of Anthony's career shows similar improvements in team performance, which suggests the effect is real.

To be clear, this kind of analysis is the kind that, I'd argue, works great -- comparing the team's performance with the player and without him. What I think *doesn't* work is just using the raw shooting percentages. Because how do you know what those percentages mean? Suppose one team is all at .460, and another team is all at .490. The .490 means that you have more players on the team above average than below average. But, the above average players are lifting the percentages of the below average players, and the below-average players are reducing the percentages of the above-average players. But which are which? We have no way of telling.

Here's a hockey example. Of Luc Robitaille's eight highest-scoring NHL seasons, six of them came while he was a teammate of Wayne Gretzky. In 1990-91, Robitaille finished with 101 points. How much of the credit for those points do you give to Robitaille, and how much of the credit do you give to Gretzky? There's no way to tell from the single season raw totals, is there? You have to know something about Robitaille, and Gretzky, and the rest of their careers, before you can give a decent estimate. And your estimate will be that Gretzky that should get some of the credit for some of Robitaille's performance.

Similarly, when Carmelo Anthony increases all his teammates' shooting percentages by 30 points, *and it's the teammates that get most of that credit* ... that's a serious problem with the stat, isn't it?

------

So far, we've only found problems with two components of player performance -- rebounds and shooting percentage. However, those are the two biggest factors that go into a player's evaluation. And, additionally, you could argue that the same thing applies to some of the other stats.

For instance, blocked shots: those are primarily a function of opportunity, aren't they? Some players take a lot more shots than others, so the guy who defends against Allen Iverson is going to block a lot more shots than his teammates, all else being equal.

------

Still, it could be possible that the problems aren't that big, and that, while the new statistics aren't perfect, they're still better than existing statistics. That's quite reasonable. However, I think that, given the obvious problems, the burden of proof shifts to those who maintain the stats still work.

The one piece of evidence that I know of, with regard to that issue, is the famous study from David Lewin and Dan Rosenbaum. It's called "The Pot Calling the Kettle Black – Are NBA Statistical Models More Irrational than 'Irrational' Decision Makers?" (I wrote about it here; you can find it online here; and you can read a David Berri critique of it here.)

What Lewin and Rosenbaum did was try to predict how teams would perform last year, based on their previous year's statistics. If the new sabermetric statistics were better evaluators of talent than, say, just points per game, they should predict better.

They didn't. Here are the authors' correlations:

0.823 -- Minutes per game
0.817 -- Points per game
0.820 -- NBA Efficiency
0.805 -- Player Efficiency Rating
0.803 -- Wins Produced
0.829 -- Alternate Win Score

As you can see, "minutes per game" -- which is probably the closest representation you can get to what the coach thinks of a player's skill -- was the second highest of all the measures. And the new stats were nothing special, although "Alternate Win Score" did come out on top. Notably, even "points per game," widely derided by most analysts, finished better than PER and Berri's "Wins Produced."

When this study came out, I thought part of the problem was that the new statistics don't measure defense, but "minutes per game" does, in a roundabout way (good defensive players will be given more minutes by their coach). I still think that. But, now, I think part of the problem is that the new statistics don't properly measure offense, either. They just aren't able to do a good job of judging how much of the team's offensive performance to allocate to the individual players.

Now that I think I understand why Lewin and Rosenbaum got the results they did, I have come to agree with their conclusions. Correct me if I'm wrong, but logic and evidence seem to say that sabermetric basketball statistics simply do not work very well for players.

-----

UPDATE: some commenters in the blogosphere are assuming that I mean that basketball sabermetric research can't work for basketball. That's not what I mean. I'm referring here only to the "formula" type stats.

I think the "plus-minus"-type approaches, like those in the Carmelo Anthony section of the post above, are quite valid, if you have a big enough sample to be meaningful.

But, just picking up a box score or looking up standard player stats online, and trying from that which players are how much better than others (the approach that "Wins Produced" and other stats take) ... well, I don't think you're ever going to be able to make that work.

UPDATE: I found a slight problem with the data: one team was missing and one team I entered twice. I've updated the post. The conclusions don't change.

For the record, the wrong slopes were .30/.39/.31/.25/.24. The corrected slopes, as above, are .30/.40/.26/.28/.27.

The wrong correlations were .59/.58/.37/.26/.40. The corrected correlations are .63/.62/.27/.27/.43.

but i am not buying into silver's perspective.

firstly winning 48 games even in a loaded western conference means less than he wants it to for the mere fact that the league is diluted and half the teams are going to be below .500 teams that a slightly better than mediocre team should beat. and frankly, given the nuggets almost complete underachieving in the playoffs, we should not look at 48 regular-season wins as any major accomplishment. yet he uses this amount of regular-season winning as a premise for the rest of the article.

in particular this material about carmelo making others more efficient while he himself remains inefficient is counter-intuitive in the extreme to the point that i have to question the validity of these statistics. or if it is valid then i would simply say that we have a player on this team that accomplishes much the same thing so far as making others more efficient just by his presence on the floor and with one half the usage rate. that's an important factor silver neglected to include: the relationship between usage rate and efficiency and the ability to make others more efficient. or as you said any decent offensive player should have this effect on his teammates, moreover be an efficient offensive player himself and with a strong TS%.

again, in my opinion the trouble is that with anthony his usage rate is through the roof and though he allegedly makes others more efficient in the regular season by his presence, he should be making them better than he does given how much of the time the ball is in his hands, and doing so through a higher assist average.

long story short how well does carmelo's game (1) mesh with others and (2) how much better does he ACTIVELY make his teammates given a 33.4% usage rate? the answer is (1) "not very well" and (2) "not enough," respectively. your best player should be better at making others better and if he doesn't then you can certainly expect more failure than success in the playoffs where you play better than average teams.

if carmelo comes to play for d'antoni with a shoot first power forward whose game is extended to 20 feet and who himself has a 31% usage rate, a shoot first point guard whose game is extended to beyond the 3-point line with a 22.6% usage rate who doesn't maintain his dribble, where will he fit in, since he needs the ball in his hands to be effective, being basically an isolation player?

does anyone envision carmelo adjusting his game without becoming disgruntled or envision d'antoni adapting his offense to suit the proclivities of carmelo anthony? i sure as hell don't.

walsh is unlikely to trade for carmelo. just reading the newspaper this morning you can sense he is annoyed with the constant questions about carmelo. i'd be annoyed too if people constantly asked me about a player that doesn't address a clear need and if my agenda is to patiently build a team, which precludes gutting it.

and if we renounce wilson chandler as a result in order to acquire anthony that still doesn't address the much more pressing needs of a backup point guard and a defensive presence in the middle. -

See more at: http://forums.realgm.com/boards/viewtopic.php?f=24&t=1084207#sthash.ZvI3hbYF.dpuf

dk7th wrote:

CrushAlot wrote:

Thursday, January 20, 2011


Sabermetric basketball statistics are too flawed to work

You know all those player evaluation statistics in basketball, like "Wins Produced," "Player Evaluation Rating," and so forth? I don't think they work. I've been thinking about it, and I don't think I trust any of them enough put much faith in their results.

That's the opposite of how I feel about baseball. For baseball, if the sportswriter consensus is that player A is an excellent offensive player, but it turns out his OPS is a mediocre .700, I'm going to trust OPS. But, for basketball, if the sportswriters say a guy's good, but his "Wins Produced" is just average, I might be inclined to trust the sportswriters.

I don't think the stats work well enough to be useful.

I'm willing to be proven wrong. A lot of basketball analysts, all of whom know a lot more about basketball than I do (and many of whom are a lot smarter than I am), will disagree. I know they'll disagree because they do, in fact, use the stats. So, there are probably arguments I haven't considered. Let me know what those are, and let me know if you think my own logic is flawed.

------

The most obvious problem is rebounds, which I've posted about many times (including these posts over the last couple of weeks). The problem is that a large proportion of rebounds are "taken" from teammates, in the sense that if the player credited with the rebound hadn't got it, another teammate would have.

We don't know the exact numbers, but maybe 70% of defensive and 50% of offensive rebounds are taken from a teammates' total.

More importantly, it's not random, and it's not the same for all players. Some rebounders will cover much more of other players' territory than others. So when player X had a huge rebounding total, we don't know whether he's just good at rebounds, whether he's just taking them from teammates, or whether it's some combination of the two.

So, even if we decide to take 70% of every defensive rebound, and assign it to teammates, we don't know that's the right number for the particular team and rebounder. This would lead to potentially large errors in player evaluations.

The bottom line: we know exactly what a rebound is worth for a team, but we don't know which players are responsible, in what proportion, for the team's overall performance.

------

Now, that's just rebounds. If that were all there were, we could just leave that out of the statistic, and go with what we have. But there's a similar problem with shooting accuracy.

I ran the same test for shooting that I ran for rebounds. For the 2008-09 season, I ran regression for each of the five positions. Each row of the regression was a single team for that year, and I checked how each position's shooting (measured by eFG%) affected the average of the other four positions (the simple average, not weighted by attempts).

It turns out that there is a strong positive correlation in shooting percentage among teammates. If one teammate shoots accurately, the rest of the team gets carried along.

Here are the numbers (updated, see end of post):

PG: slope 0.30, correlation 0.63
SG: slope 0.40, correlation 0.62
SF: slope 0.26, correlation 0.27
PF: slope 0.28, correlation 0.27
-C: slope 0.27, correlation 0.43

To read one line off the chart: for every one percentage point increase in shooting percentage by the SF (say, from 47% to 48%), you saw an increase of 0.26% in each of his teammates (say, from 47% to 47.26%).

The coefficients are a lot more important than they look at first glance, because they represent a change in the average of all four teammates. Suppose all five teammates took the same number of shots (which they don't, but never mind right now). That means that when the SF makes one extra field goal, each teammate also makes an extra 0.26, for a team team total of 1.04 extra field goals.

That's a huge effect.

And, it makes sense, if my logic is right (correct me if I'm wrong). Suppose you have a team where everyone has a talent of .450, but then you get a new guy on the team (player X) with a talent of .550. You're going to want him to shoot more often than the other players. For instance, if X and another guy are equally open for a roughly equal shot, you're going to want to give the ball to X. Even if Y is a little more open than X, you'll figure that X will still outshoot Y -- maybe not .550 to .450, but, in this situation, maybe .500 to .450. So X gets the ball more often.

But, then, the defense will concentrate a little more on X, and a little less on the .450 guys. That means X might see his percentage drop from .550 to .500, say. But the extra attention to X creates more open shots for the .450 guys, and they improve to (say) .480 each.

Most of the new statistics simply treat FG% as if it's solely the achievement of the player taking the shot, when, it seems, it is very significantly influenced by his teammates.

------

Some of that, of course, might be that teams with good players tend to have other good players; that is, it's all correlation, and not causation. But there's evidence that's not the case, as illustrated by a recent debate on the value of Carmelo Anthony.

Last week, Nate Silver showed that if you looked at Carmelo Anthony's teammates' performance, and then looked at that performance when Anthony wasn't on their team, you see a difference of .038 in shooting percentage. That's huge -- about 15 wins a season.

Dave Berri responded with three criticisms. First, that Silver weighted by player instead of by game; second, that Silver hadn't considered the age of the teammates (since very young players improve anyway as they get older); and, third, that if you control for age and a bunch of other things, the results aren't statistically significant from zero. (However, Berri didn't post the full regression results, and did not claim that his estimate was different from .038.)

Finally, over at Basketball Prospectus, Kevin Pelton ran a similar analysis, but within games instead of between seasons (which eliminates the age problem, and a bunch of other possible confounding variables). He found a difference of .028. Not quite as high as Silver, but still pretty impressive. Furthermore, a similar analysis of all of Anthony's career shows similar improvements in team performance, which suggests the effect is real.

To be clear, this kind of analysis is the kind that, I'd argue, works great -- comparing the team's performance with the player and without him. What I think *doesn't* work is just using the raw shooting percentages. Because how do you know what those percentages mean? Suppose one team is all at .460, and another team is all at .490. The .490 means that you have more players on the team above average than below average. But, the above average players are lifting the percentages of the below average players, and the below-average players are reducing the percentages of the above-average players. But which are which? We have no way of telling.

Here's a hockey example. Of Luc Robitaille's eight highest-scoring NHL seasons, six of them came while he was a teammate of Wayne Gretzky. In 1990-91, Robitaille finished with 101 points. How much of the credit for those points do you give to Robitaille, and how much of the credit do you give to Gretzky? There's no way to tell from the single season raw totals, is there? You have to know something about Robitaille, and Gretzky, and the rest of their careers, before you can give a decent estimate. And your estimate will be that Gretzky that should get some of the credit for some of Robitaille's performance.

Similarly, when Carmelo Anthony increases all his teammates' shooting percentages by 30 points, *and it's the teammates that get most of that credit* ... that's a serious problem with the stat, isn't it?

------

So far, we've only found problems with two components of player performance -- rebounds and shooting percentage. However, those are the two biggest factors that go into a player's evaluation. And, additionally, you could argue that the same thing applies to some of the other stats.

For instance, blocked shots: those are primarily a function of opportunity, aren't they? Some players take a lot more shots than others, so the guy who defends against Allen Iverson is going to block a lot more shots than his teammates, all else being equal.

------

Still, it could be possible that the problems aren't that big, and that, while the new statistics aren't perfect, they're still better than existing statistics. That's quite reasonable. However, I think that, given the obvious problems, the burden of proof shifts to those who maintain the stats still work.

The one piece of evidence that I know of, with regard to that issue, is the famous study from David Lewin and Dan Rosenbaum. It's called "The Pot Calling the Kettle Black – Are NBA Statistical Models More Irrational than 'Irrational' Decision Makers?" (I wrote about it here; you can find it online here; and you can read a David Berri critique of it here.)

What Lewin and Rosenbaum did was try to predict how teams would perform last year, based on their previous year's statistics. If the new sabermetric statistics were better evaluators of talent than, say, just points per game, they should predict better.

They didn't. Here are the authors' correlations:

0.823 -- Minutes per game
0.817 -- Points per game
0.820 -- NBA Efficiency
0.805 -- Player Efficiency Rating
0.803 -- Wins Produced
0.829 -- Alternate Win Score

As you can see, "minutes per game" -- which is probably the closest representation you can get to what the coach thinks of a player's skill -- was the second highest of all the measures. And the new stats were nothing special, although "Alternate Win Score" did come out on top. Notably, even "points per game," widely derided by most analysts, finished better than PER and Berri's "Wins Produced."

When this study came out, I thought part of the problem was that the new statistics don't measure defense, but "minutes per game" does, in a roundabout way (good defensive players will be given more minutes by their coach). I still think that. But, now, I think part of the problem is that the new statistics don't properly measure offense, either. They just aren't able to do a good job of judging how much of the team's offensive performance to allocate to the individual players.

Now that I think I understand why Lewin and Rosenbaum got the results they did, I have come to agree with their conclusions. Correct me if I'm wrong, but logic and evidence seem to say that sabermetric basketball statistics simply do not work very well for players.

-----

UPDATE: some commenters in the blogosphere are assuming that I mean that basketball sabermetric research can't work for basketball. That's not what I mean. I'm referring here only to the "formula" type stats.

I think the "plus-minus"-type approaches, like those in the Carmelo Anthony section of the post above, are quite valid, if you have a big enough sample to be meaningful.

But, just picking up a box score or looking up standard player stats online, and trying from that which players are how much better than others (the approach that "Wins Produced" and other stats take) ... well, I don't think you're ever going to be able to make that work.

UPDATE: I found a slight problem with the data: one team was missing and one team I entered twice. I've updated the post. The conclusions don't change.

For the record, the wrong slopes were .30/.39/.31/.25/.24. The corrected slopes, as above, are .30/.40/.26/.28/.27.

The wrong correlations were .59/.58/.37/.26/.40. The corrected correlations are .63/.62/.27/.27/.43.

http://blog.philbirnbaum.com/2011/01/sabermetric-basketball-statistics-are.html

his entire argument breaks down because of these flawed suppositions. you can't create a vacuum scenario for statistical analysis. the real world contains air.